Overview

Dataset statistics

Number of variables13
Number of observations26792
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory105.0 B

Variable types

Categorical1
Numeric10
Boolean1
DateTime1

Alerts

Year has constant value "2019"Constant
is_day has constant value "True"Constant
DHI is highly overall correlated with GHI and 1 other fieldsHigh correlation
DNI is highly overall correlated with GHI and 1 other fieldsHigh correlation
GHI is highly overall correlated with DHI and 2 other fieldsHigh correlation
Relative Humidity is highly overall correlated with TemperatureHigh correlation
Solar Zenith Angle is highly overall correlated with DHI and 2 other fieldsHigh correlation
Temperature is highly overall correlated with Relative HumidityHigh correlation
Datetime has unique valuesUnique
Minute has 4471 (16.7%) zerosZeros
DHI has 1881 (7.0%) zerosZeros
DNI has 3437 (12.8%) zerosZeros
GHI has 1881 (7.0%) zerosZeros

Reproduction

Analysis started2023-11-05 06:56:43.334446
Analysis finished2023-11-05 06:57:03.389631
Duration20.06 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2019
26792 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters107168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 26792
100.0%

Length

2023-11-05T12:27:03.490652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T12:27:03.603547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 26792
100.0%

Most occurring characters

ValueCountFrequency (%)
2 26792
25.0%
0 26792
25.0%
1 26792
25.0%
9 26792
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107168
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 26792
25.0%
0 26792
25.0%
1 26792
25.0%
9 26792
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 26792
25.0%
0 26792
25.0%
1 26792
25.0%
9 26792
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 26792
25.0%
0 26792
25.0%
1 26792
25.0%
9 26792
25.0%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4575246
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:03.706423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2399911
Coefficient of variation (CV)0.50173887
Kurtosis-1.0527107
Mean6.4575246
Median Absolute Deviation (MAD)3
Skewness0.023194774
Sum173010
Variance10.497543
MonotonicityIncreasing
2023-11-05T12:27:03.801620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 2678
10.0%
5 2633
9.8%
6 2628
9.8%
8 2520
9.4%
4 2371
8.8%
3 2239
8.4%
9 2239
8.4%
10 2103
7.8%
1 1869
7.0%
11 1854
6.9%
Other values (2) 3658
13.7%
ValueCountFrequency (%)
1 1869
7.0%
2 1837
6.9%
3 2239
8.4%
4 2371
8.8%
5 2633
9.8%
6 2628
9.8%
7 2678
10.0%
8 2520
9.4%
9 2239
8.4%
10 2103
7.8%
ValueCountFrequency (%)
12 1821
6.8%
11 1854
6.9%
10 2103
7.8%
9 2239
8.4%
8 2520
9.4%
7 2678
10.0%
6 2628
9.8%
5 2633
9.8%
4 2371
8.8%
3 2239
8.4%

Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.728202
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:03.941323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7977341
Coefficient of variation (CV)0.55936043
Kurtosis-1.1939322
Mean15.728202
Median Absolute Deviation (MAD)8
Skewness0.0063629486
Sum421390
Variance77.400126
MonotonicityNot monotonic
2023-11-05T12:27:04.079684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 883
 
3.3%
28 883
 
3.3%
22 882
 
3.3%
23 882
 
3.3%
21 882
 
3.3%
7 882
 
3.3%
17 882
 
3.3%
19 881
 
3.3%
18 881
 
3.3%
24 881
 
3.3%
Other values (21) 17973
67.1%
ValueCountFrequency (%)
1 879
3.3%
2 879
3.3%
3 880
3.3%
4 881
3.3%
5 883
3.3%
6 881
3.3%
7 882
3.3%
8 881
3.3%
9 879
3.3%
10 878
3.3%
ValueCountFrequency (%)
31 510
1.9%
30 811
3.0%
29 814
3.0%
28 883
3.3%
27 881
3.3%
26 880
3.3%
25 881
3.3%
24 881
3.3%
23 882
3.3%
22 882
3.3%

Hour
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.969655
Minimum5
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:04.210561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q315
95-th percentile18
Maximum20
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6722624
Coefficient of variation (CV)0.30679768
Kurtosis-1.0487346
Mean11.969655
Median Absolute Deviation (MAD)3
Skewness0.075828131
Sum320691
Variance13.485511
MonotonicityNot monotonic
2023-11-05T12:27:04.346494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 2190
8.2%
9 2190
8.2%
10 2190
8.2%
11 2190
8.2%
12 2190
8.2%
13 2190
8.2%
14 2190
8.2%
15 2190
8.2%
7 2186
8.2%
16 2034
7.6%
Other values (6) 5052
18.9%
ValueCountFrequency (%)
5 230
 
0.9%
6 1316
4.9%
7 2186
8.2%
8 2190
8.2%
9 2190
8.2%
10 2190
8.2%
11 2190
8.2%
12 2190
8.2%
13 2190
8.2%
14 2190
8.2%
ValueCountFrequency (%)
20 31
 
0.1%
19 696
 
2.6%
18 1232
4.6%
17 1547
5.8%
16 2034
7.6%
15 2190
8.2%
14 2190
8.2%
13 2190
8.2%
12 2190
8.2%
11 2190
8.2%

Minute
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.040311
Minimum0
Maximum50
Zeros4471
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:04.454829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q340
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.094761
Coefficient of variation (CV)0.68268964
Kurtosis-1.270323
Mean25.040311
Median Absolute Deviation (MAD)20
Skewness-0.0040708679
Sum670880
Variance292.23084
MonotonicityNot monotonic
2023-11-05T12:27:04.574443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50 4490
16.8%
30 4474
16.7%
40 4472
16.7%
0 4471
16.7%
20 4443
16.6%
10 4442
16.6%
ValueCountFrequency (%)
0 4471
16.7%
10 4442
16.6%
20 4443
16.6%
30 4474
16.7%
40 4472
16.7%
50 4490
16.8%
ValueCountFrequency (%)
50 4490
16.8%
40 4472
16.7%
30 4474
16.7%
20 4443
16.6%
10 4442
16.6%
0 4471
16.7%

DHI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct495
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.262504
Minimum0
Maximum499
Zeros1881
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:04.698956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median82
Q3111
95-th percentile285
Maximum499
Range499
Interquartile range (IQR)55

Descriptive statistics

Standard deviation82.428428
Coefficient of variation (CV)0.83040851
Kurtosis4.8731611
Mean99.262504
Median Absolute Deviation (MAD)28
Skewness2.0071733
Sum2659441
Variance6794.4458
MonotonicityNot monotonic
2023-11-05T12:27:04.880554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1881
 
7.0%
68 300
 
1.1%
72 292
 
1.1%
76 289
 
1.1%
73 288
 
1.1%
93 286
 
1.1%
66 285
 
1.1%
77 283
 
1.1%
74 281
 
1.0%
75 280
 
1.0%
Other values (485) 22327
83.3%
ValueCountFrequency (%)
0 1881
7.0%
3 6
 
< 0.1%
4 20
 
0.1%
5 26
 
0.1%
6 43
 
0.2%
7 41
 
0.2%
8 35
 
0.1%
9 30
 
0.1%
10 40
 
0.1%
11 75
 
0.3%
ValueCountFrequency (%)
499 2
 
< 0.1%
498 3
< 0.1%
497 2
 
< 0.1%
494 3
< 0.1%
493 3
< 0.1%
492 4
< 0.1%
491 3
< 0.1%
490 1
 
< 0.1%
489 1
 
< 0.1%
488 5
< 0.1%

DNI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1038
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.02079
Minimum0
Maximum1037
Zeros3437
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:05.074534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1276
median753
Q3906
95-th percentile971
Maximum1037
Range1037
Interquartile range (IQR)630

Descriptive statistics

Standard deviation354.88275
Coefficient of variation (CV)0.59046669
Kurtosis-1.159793
Mean601.02079
Median Absolute Deviation (MAD)191
Skewness-0.63886
Sum16102549
Variance125941.77
MonotonicityNot monotonic
2023-11-05T12:27:05.281715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3437
 
12.8%
946 110
 
0.4%
934 99
 
0.4%
953 97
 
0.4%
941 96
 
0.4%
944 95
 
0.4%
956 95
 
0.4%
942 94
 
0.4%
900 94
 
0.4%
935 93
 
0.3%
Other values (1028) 22482
83.9%
ValueCountFrequency (%)
0 3437
12.8%
1 17
 
0.1%
2 25
 
0.1%
3 24
 
0.1%
4 20
 
0.1%
5 13
 
< 0.1%
6 21
 
0.1%
7 20
 
0.1%
8 16
 
0.1%
9 21
 
0.1%
ValueCountFrequency (%)
1037 1
 
< 0.1%
1036 4
< 0.1%
1035 2
 
< 0.1%
1034 2
 
< 0.1%
1033 2
 
< 0.1%
1032 1
 
< 0.1%
1031 2
 
< 0.1%
1030 2
 
< 0.1%
1029 1
 
< 0.1%
1028 6
< 0.1%

GHI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1062
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455.09533
Minimum0
Maximum1063
Zeros1881
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:05.490297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1172
median446
Q3718
95-th percentile968
Maximum1063
Range1063
Interquartile range (IQR)546

Descriptive statistics

Standard deviation314.09852
Coefficient of variation (CV)0.69018183
Kurtosis-1.2023298
Mean455.09533
Median Absolute Deviation (MAD)273
Skewness0.12601269
Sum12192914
Variance98657.883
MonotonicityNot monotonic
2023-11-05T12:27:05.687923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1881
 
7.0%
15 70
 
0.3%
42 53
 
0.2%
17 50
 
0.2%
33 50
 
0.2%
14 49
 
0.2%
18 49
 
0.2%
26 48
 
0.2%
13 48
 
0.2%
31 47
 
0.2%
Other values (1052) 24447
91.2%
ValueCountFrequency (%)
0 1881
7.0%
3 6
 
< 0.1%
4 20
 
0.1%
5 26
 
0.1%
6 43
 
0.2%
7 41
 
0.2%
8 35
 
0.1%
9 28
 
0.1%
10 33
 
0.1%
11 38
 
0.1%
ValueCountFrequency (%)
1063 3
< 0.1%
1062 3
< 0.1%
1061 2
 
< 0.1%
1060 4
< 0.1%
1059 3
< 0.1%
1058 4
< 0.1%
1057 2
 
< 0.1%
1056 7
< 0.1%
1055 2
 
< 0.1%
1054 4
< 0.1%

Relative Humidity
Real number (ℝ)

Distinct5818
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.742811
Minimum3.16
Maximum92.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:05.899438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.16
5-th percentile6.28
Q111.53
median18.74
Q331.9925
95-th percentile57.149
Maximum92.14
Range88.98
Interquartile range (IQR)20.4625

Descriptive statistics

Standard deviation16.290496
Coefficient of variation (CV)0.68612329
Kurtosis1.3702742
Mean23.742811
Median Absolute Deviation (MAD)8.96
Skewness1.276409
Sum636117.4
Variance265.38026
MonotonicityNot monotonic
2023-11-05T12:27:06.106057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.83 30
 
0.1%
8.42 28
 
0.1%
6.73 27
 
0.1%
14.57 25
 
0.1%
7.56 24
 
0.1%
8.98 23
 
0.1%
7.45 23
 
0.1%
12.18 23
 
0.1%
19.91 23
 
0.1%
8.91 23
 
0.1%
Other values (5808) 26543
99.1%
ValueCountFrequency (%)
3.16 1
 
< 0.1%
3.19 4
< 0.1%
3.21 2
 
< 0.1%
3.23 2
 
< 0.1%
3.25 4
< 0.1%
3.26 1
 
< 0.1%
3.27 3
< 0.1%
3.28 1
 
< 0.1%
3.29 7
< 0.1%
3.31 1
 
< 0.1%
ValueCountFrequency (%)
92.14 1
< 0.1%
91.51 1
< 0.1%
91.15 1
< 0.1%
90.27 1
< 0.1%
90.02 1
< 0.1%
89.61 1
< 0.1%
89.44 2
< 0.1%
89 1
< 0.1%
88.83 1
< 0.1%
88.32 1
< 0.1%

Solar Zenith Angle
Real number (ℝ)

Distinct8140
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.389402
Minimum12.73
Maximum103.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size418.6 KiB
2023-11-05T12:27:06.316385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.73
5-th percentile21.4855
Q143.26
median59.54
Q373.97
95-th percentile91.87
Maximum103.23
Range90.5
Interquartile range (IQR)30.71

Descriptive statistics

Standard deviation21.029689
Coefficient of variation (CV)0.36016278
Kurtosis-0.68573604
Mean58.389402
Median Absolute Deviation (MAD)15.23
Skewness-0.13698239
Sum1564368.9
Variance442.24783
MonotonicityNot monotonic
2023-11-05T12:27:06.537049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.12 16
 
0.1%
59.61 14
 
0.1%
62.13 13
 
< 0.1%
60.07 13
 
< 0.1%
59.03 13
 
< 0.1%
54.29 13
 
< 0.1%
56.04 13
 
< 0.1%
59.25 13
 
< 0.1%
49.74 12
 
< 0.1%
58.04 12
 
< 0.1%
Other values (8130) 26660
99.5%
ValueCountFrequency (%)
12.73 2
< 0.1%
12.74 2
< 0.1%
12.75 1
 
< 0.1%
12.76 1
 
< 0.1%
12.77 1
 
< 0.1%
12.78 1
 
< 0.1%
12.8 1
 
< 0.1%
12.81 1
 
< 0.1%
12.83 3
< 0.1%
12.84 4
< 0.1%
ValueCountFrequency (%)
103.23 1
< 0.1%
103.19 1
< 0.1%
103.15 1
< 0.1%
103.07 1
< 0.1%
103 1
< 0.1%
102.96 1
< 0.1%
102.95 1
< 0.1%
102.94 1
< 0.1%
102.92 2
< 0.1%
102.89 2
< 0.1%

Temperature
Real number (ℝ)

Distinct453
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.550657
Minimum-1.1
Maximum44.9
Zeros1
Zeros (%)< 0.1%
Negative5
Negative (%)< 0.1%
Memory size418.6 KiB
2023-11-05T12:27:06.809872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1
5-th percentile7.6
Q115.7
median24.6
Q333.6
95-th percentile41.1
Maximum44.9
Range46
Interquartile range (IQR)17.9

Descriptive statistics

Standard deviation10.715733
Coefficient of variation (CV)0.43647438
Kurtosis-1.0676748
Mean24.550657
Median Absolute Deviation (MAD)9
Skewness-0.05373519
Sum657761.2
Variance114.82693
MonotonicityNot monotonic
2023-11-05T12:27:07.037611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.2 117
 
0.4%
32.3 113
 
0.4%
26.5 104
 
0.4%
22.3 104
 
0.4%
26.7 102
 
0.4%
24.4 101
 
0.4%
31.1 100
 
0.4%
25.3 98
 
0.4%
25.9 97
 
0.4%
24.1 97
 
0.4%
Other values (443) 25759
96.1%
ValueCountFrequency (%)
-1.1 1
 
< 0.1%
-0.7 1
 
< 0.1%
-0.4 2
< 0.1%
-0.1 1
 
< 0.1%
0 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 2
< 0.1%
0.3 2
< 0.1%
0.5 4
< 0.1%
0.6 1
 
< 0.1%
ValueCountFrequency (%)
44.9 6
 
< 0.1%
44.8 5
 
< 0.1%
44.7 3
 
< 0.1%
44.6 5
 
< 0.1%
44.5 6
 
< 0.1%
44.4 6
 
< 0.1%
44.3 8
 
< 0.1%
44.2 22
0.1%
44.1 10
< 0.1%
44 13
< 0.1%

is_day
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.5 KiB
True
26792 
ValueCountFrequency (%)
True 26792
100.0%
2023-11-05T12:27:07.189705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct26792
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size418.6 KiB
Minimum2019-01-01 07:00:00
Maximum2019-12-31 16:30:00
2023-11-05T12:27:07.350410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:07.547431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-11-05T12:27:01.134518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:44.022022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.200651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.863109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:49.620077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.489180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:53.434658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:55.447747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:57.350197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:59.298372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:01.317568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:44.735098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.361215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.020789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:49.804031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.652428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:53.645839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:55.626123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:57.526076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:59.482919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:01.483891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:44.877752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.518651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.177861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:50.000538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.826248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:53.849838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:55.820552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:57.719037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:59.688020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:01.650133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.034954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.709606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.338626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:50.205831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:52.214485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:54.042093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.023778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:57.931865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:59.851065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:01.792373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.209908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.873496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.485935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:50.435181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:52.388381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:54.228950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.208785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:58.116040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.016755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:01.942426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.377609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.042071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.651533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:50.603923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:52.558114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:54.466975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.404647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:58.297791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.187079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:02.319300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.541779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.204458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:48.845174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:50.801436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:52.754398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:54.665883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.589878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:58.540730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.358693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:02.458964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.710620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.384871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:49.029782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.005684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:52.926656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:54.851136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.790568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:58.731623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.542988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:02.606666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:45.872725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.570397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:49.236830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.187300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:53.089219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:55.054209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:56.960078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:58.914248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.726575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:02.755493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:46.045960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:47.705499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:49.427984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:51.340058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:53.260381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:55.238093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:57.164922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:26:59.127992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-05T12:27:00.917652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-05T12:27:07.751099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
MonthDayHourMinuteDHIDNIGHIRelative HumiditySolar Zenith AngleTemperature
Month1.000-0.006-0.019-0.001-0.1410.090-0.002-0.3590.0550.246
Day-0.0061.0000.0060.0010.000-0.006-0.0070.0540.000-0.034
Hour-0.0190.0061.000-0.074-0.236-0.285-0.293-0.1550.2740.154
Minute-0.0010.001-0.0741.000-0.003-0.002-0.002-0.0030.0040.004
DHI-0.1410.000-0.236-0.0031.0000.2480.665-0.129-0.7880.279
DNI0.090-0.006-0.285-0.0020.2481.0000.834-0.483-0.6270.270
GHI-0.002-0.007-0.293-0.0020.6650.8341.000-0.472-0.9220.459
Relative Humidity-0.3590.054-0.155-0.003-0.129-0.483-0.4721.0000.383-0.736
Solar Zenith Angle0.0550.0000.2740.004-0.788-0.627-0.9220.3831.000-0.461
Temperature0.246-0.0340.1540.0040.2790.2700.459-0.736-0.4611.000

Missing values

2023-11-05T12:27:02.960047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-05T12:27:03.226643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearMonthDayHourMinuteDHIDNIGHIRelative HumiditySolar Zenith AngleTemperatureis_dayDatetime
4220191170112451530.4988.97-0.4True2019-01-01 07:00:00
43201911710173753429.8487.36-0.1True2019-01-01 07:10:00
44201911720224755829.1985.720.2True2019-01-01 07:20:00
45201911730275558528.6084.070.5True2019-01-01 07:30:00
462019117403262011325.9182.440.9True2019-01-01 07:40:00
472019117503567314325.3680.841.2True2019-01-01 07:50:00
48201911803971717224.8279.261.5True2019-01-01 08:00:00
492019118104275520224.2977.731.8True2019-01-01 08:10:00
502019118204478723223.9576.222.0True2019-01-01 08:20:00
512019118304781426123.6174.772.2True2019-01-01 08:30:00
YearMonthDayHourMinuteDHIDNIGHIRelative HumiditySolar Zenith AngleTemperatureis_dayDatetime
52506201912311504279824737.8175.1510.2True2019-12-31 15:00:00
525072019123115104077021937.8176.6110.2True2019-12-31 15:10:00
525082019123115203873719037.8178.1210.2True2019-12-31 15:20:00
525092019123115303569916137.8179.6710.2True2019-12-31 15:30:00
525102019123115403265413237.8181.2510.2True2019-12-31 15:40:00
525112019123115502960010337.8182.8510.2True2019-12-31 15:50:00
52512201912311603323330.4184.454.4True2019-12-31 16:00:00
525132019123116102442431.0586.104.1True2019-12-31 16:10:00
525142019123116201201231.7187.743.8True2019-12-31 16:20:00
5251520191231163000032.1689.333.6True2019-12-31 16:30:00